Lilia Alberghina Dept. of Biotechnology and Biosciences University of Milano-Bicocca Milan, Italy

Lilia AlberghinaDept. of Biotechnology and Biosciences

University of Milano-Bicocca Milan, Italy

Seminar at CNR-IASIRome, Dec 18, 2008

SYSTEM-LEVEL PROPERTIES OF CELL CYCLE NETWORKS

THE GREAT CHALLENGE FOR 21st CENTURY BIOLOGY

• Only rarely a cellular function is determined by an individual gene product, but more often it is determined by the dynamic interaction of hundreds or thousands of gene products making it difficult to fully understand biological functions at a molecular level.

• As a first step, it is necessary to identify the structure and dynamics of networks that execute and control basic complex cellular functions (metabolism, growth, cycle, differentiation, death, senescence, transformation).

2

Systems Biology

SYSTEMS BIOLOGY

• Systems Biology concerns the mechanisms by which macromolecules interact dynamically to produce the functional properties of living cells.

• It integrates molecular analysis with mathematical modeling and simulations

• Cellular processes can be dissected into modules: subsystems of interacting molecules (DNA, RNA, proteins, small molecules) that perform a given task in a way that is largely independent from the context.

• Modularity is organized by “global connectors” among modules and by “party hubs” that connect partners of each module.

• The function of each system derives as an emergent property from interactions of the various elements of its network.

• Biological networks are robust, since they are mostly able to maintain their function despite external and internal perturbations.

3

SYSTEM-LEVEL PROPERTIES

SYSTEMS BIOLOGY OF THE CELL CYCLE

Essential functions of cell cycle:

• coordination between growth and cycle progression

• fidelity in nuclear genome replication and transmission

homeostasis of cell size

setting the critical cell size required to enter S phase (Ps)

trigger a coherent, synchronous onset of DNA replication

Our task has been, using a modular approach, to analyse the role of the G1/S network in determining these functions

4

A

B

GROWTH, CYCLE AND Ps

5

G1 S G2 MPs

In the budding yeast Saccharomyces cerevisiae

• During evolution the sequences of many cell cycle components are conserved from yeast to humans

T = ln 2/λ

2 –TP/T +2 –TD/T =1

A

FROM A TOP-DOWN MODEL OF CELL CYCLE TO NETWORK IDENTIFICATION OF THE G1 TO S TRANSITION

o Alberghina et al, Oncogene 20, 1128-1134, 2001o Alberghina et al, Current Genomics 5, 615-627, 2004

The G1 to S transition is controlled by a cell sizer that involves Cki and is modulated by growth rate

• Involvement of the Cki Far1 in mitotic cell cycle

o Alberghina et al, J. Cell. Biol. 167, 433-443, 2004

• Role of nucleo/cytoplasmic localization of Sic1 for G1 to S transition

o Rossi et al, Cell Cycle 4, 1798-1807, 20056

• A top-down mathematical model of cell cycle

MATHEMATICAL MODEL OF THE G1 TO S TRANSITION

STARTING FROM SMALL DAUGHTER CELLS

7Barberis M, Klipp E. Vanoni M. and Alberghina L., PLoS Comput. Biol., 3, e64, 2007

Cln3 made in G1 proportional to cell mass

Far1

Cln3.CdK1

Cell sizer

Whi5SBF/MBF

Cln1.2. CdK1

Clb5.6. CdK1/Sic1

Sic1 degradation

G1 to S transition

Budding

THRESHOLD

DNA replication

timer

PsA Far1 amount endowed at the previous mitotic exit

(S-Cdk)

THE MODEL: EQUATIONS AND SIMULATIONS

The model has been implemented by a set of ordinary differential equations (ODEs), that describe the temporal changes of the concentrations of the involved proteins and complexes.

The model considers the localization of components in different cell compartments (cytoplasm or nucleus) as well as the cell size growth during the G1 phase.

• Parameter identification has been done by text mining for kinetic constants, by mathematical fitting of simulated versus experimental time series, by utilization of available experimental data as input quantities, and by parameter values utilized in literature models.

The model predicts the dynamics of key cycle players and allows to estimate Ps

It accounts for a variety of genetic and nutritional growth conditions 8

THE BREAKTHROUGH: PS IS AN EMERGENT PROPERTY OF THE G1/S NETWORK

Growth rate

Far1 initial

concentration

Cln3 initial

concentration

Binding value of Sic1

to Cdk1-Clb5,6cyt

9The value of Ps increases with growth rate

WHY GROWTH RATE MODULATES Ps

This model allows us to set in a unified framework all previously proposed regulatory events for the setting of

Ps

1.00

1.20

1.40

1.60

1.80

2.00

0 20 40 60 80 100 120 140 160 180 200

Time (minutes)

Ce

ll s

ize

S phase

S phase

T1

T2

= sizer T2 – T1 = timer (experimentally determined to be about 40 min for daughter cells growing in glucoseT = 104 min)

10

INDEPENDENT CONFIRMATION OF THE SIZER-TIMER STRUCTURE OF THE G1 TO S TRANSITION

Average T2 ~ 20 min

Average T1 D ~ 20 min

Average T1 M ~ 1 min S. Di Talia et al, Nature 448, 947-951, 2007 11

single-cell imaging

40 min

HOW TO CONNECT S-Cdk ACTIVITY WITH INITIATION OF DNA REPLICATION?

• the amount of S-Cdk activity varies with the growth rate (Rossi et al, Cell Cycle, 2005)

• the rate of degradation of Sic 1 may be modulated by Ck2 phosphorylation of Cdc34 (Coccetti et al, Cell Cycle, 2008)

?

12Tanaka et al, Cell Division, 2007

B

FOR A FAITHFUL DNA REPLICATION IT HAS TO START SYNCHRONOUSLY FROM ALL INVOLVED ORC

• How does the availability of Clb5,6.Cdk1 control the onset of DNA replication in budding yeast?

13

ORIGINS OF DNA REPLICATION

14A. Brummer, V. Zinzalla, C. Salazar, L. Alberghina and T. Hoefer, 2008, submitted

MODELING THE NETWORK CONTROLLING THE ONSET OF DNA REPLICATION

R. HEINRICH

• The mathematical model, 57 equations and 44 parameters, gives the probability that a particular origin is, after a certain time t in one of the states, S0 through S7. From the complete ensemble of each replication origin, this probability translates into the fraction of origins in a given state.

• The parameters are grouped in three categories: protein concentrations (taken from Ghaemmaghani et al, 2003); binding/dissociation constants (estimated following Gabdoulline and Wade, 2001); protein phosphorylation/dephosphorylation rates (estimated following Shaw et al, 1995; Okamura et al, 2004).

• This formulation allows us to study the coherence of origin firing and the molecular parameters that influence it.

EQUATIONS AND PARAMETERS OF THE MODEL

15

STANDARD SIMULATED DYNAMICS OF INITIATION OF DNA REPLICATION AND EFFECTS OF S-Cdk

AVAILABILITY ON FIRING COHERENCE

coherent firing

sharp synchrony

coherent firing

sharp synchrony

less coherent firing

loose synchrony

16

MULTI-SITE PHOSPHORYLATION OF Sld2 by S-Cdk IS THE MOLECULAR DEVICE RESPONSIBLE FOR THE

KINETICS OF DNA FIRING

Distributive phosphorylation of Sld2 by S-Cdk

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Sld2(6Ser/Thr // Thr84) total

MULTI-SITE PHOSPHORYLATION OF Sld2 by S-Cdk IS A DECOUPLING MOLECULAR DEVICE RESPONSIBLE

FOR THE ROBUST KINETICS OF DNA FIRING

Multi-site phosphorylation of Sld2 works as a decoupling device, a robustness mechanism that isolates system’s functionality from variations of the input

• Towards retarded activation full reproducibility of standard performance

• Towards reduced activation92% of origin license and fire at low (20% of standard) S-Cdk availability

longer S phase

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KINETIC AND STRUCTURAL DETERMINANTS OF NETWORK ROBUSTNESS

• Statistical ensemble of model parameterization by randomly changing all rate constants in a 2 order of magnitude interval around the reference value

• Selection of those that satisfy > 185 origins fired within 45 min 200 admissible parameter sets

The vast majority of functional network design kinetics behave as the reference case

• prepare and fire kinetics 19

EMERGENT PROPERTIES AND ROBUSTNESS IN CELL CYCLE CONTROL

• In conclusion:

• the setting of the critical cell size at the onset of S phase is an emergent property of the G1 to S network modulated by growth rate;

• the robustness of the coherent synchronous onset of DNA replication relies on molecular design principles (chiefly the multi-site phosphorylation of Sld2) of the molecular network executing and controlling DNA firing.

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TAKE HOME LESSON

• To identify system-level properties (emergence, robustness) a well-defined molecular structure of the network is needed

• Integrated molecular/computational analysis is required to identify system-level properties

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• Only in this way can we understand the link between molecular networks and biological functions

WHAT NEXT?

Far1 Whi5 Sic1

Cln3.CdK1 SBF-MBF Clb5.CdK1

Modulation of level (synthesis/degradation)

Modulation of binding activity (phosphorylation?)

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Modulation of nucleo/cytoplasmic localization

• Systems biology concerns the mechanisms by which macromolecules interact dynamically to produce the functional properties of living cells

Perturbations by NCE

MIUR-FIRB ITALBIONET

7 FP Project

Quantitative Proteomics/

Phosphoproteomics

How does cell signalling (TOR, Ck2, Snf1/AMPK, PKA, etc.) affect the strength of binding of different interactors?

CHANGING THE GENE DOSAGE OF A KEY CYCLE PLAYER

Genome-wide changes in response to FAR1 gene dosage

PCA analysis: done at IASI/RM (Paola Bertolazzi, Giovanni Felici)

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Project ongoing

GROWTH PARAMETERS OF YEAST STRAINS WITH ALTERED FAR1 DOSAGES

Medium

Glucose Ethanol

Strain T (min)

F (%) Ps T (min) F (%) Ps

WT 104 ± 7 73 ± 6 375 ± 10 252 ± 17 57 ± 6 198 ± 10

far1 102 ± 5 74 ± 5 395 ± 65 258 ± 13 55 ± 5 270 ± 25

FAR1OE 120 ± 5 72 ± 1 457 ± 25 260 ± 23 56 ± 2 297 ± 20

far1FAR1OE

WTfar1FAR1OE

WT

24

FAR1 gene dosage: analysis of the transcriptome

Glucose

wt far1 FAR1OE

Ethanol

wt far1 FAR1OE

Expre

ssio

n+

-

25

PCA analysis separates transcriptional profiles as a function of the FAR1 gene

dosage

ethanol glucose

In glucose-grown cells PC1 (explaining over 70% of variability) separates well the three samples, while PC2 (explaining about 15% of variability) only distinguishes far1from wild type and Far1-overexpressing cells.

In ethanol-growing cells, PC1 (explaining ca. 63% of variability) does not distinguish wild type and far1 mutant cells, that are instead well separated on the PC2 axis (explaining about 20% of variability).

26

Different statistical tools identify some superimposable but also distinct FAR1-modulated

genes

PCAANOVA 143

Ethanol-grown cells

295ORFs

440ORFs

PCAANOVA 289

Glucose-grown cells

816ORFs

917ORFs

The biological evaluation is ongoing

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Effect of the FAR1 dosage on the proteome : growth in ethanol-supplemented media

exp

on

entia

l gro

wth

in S

CE

100

10

3 10

MW(Kda)

pI

Wt

Tpm1

Sgt2

Ahp1

Hsp12

Hsp26

His4;Tup1

Sbp1;Pep4

Ddr48

Rps7A

Gdh3

Hxk2

Hsc82;Hsp82

Rib3

Snz1Sec14

Sbp1

Rki1

Eno2Arg1Ino1

Leu4

Leu1

Ura1

Bat1

Pgk1

Tkl1

Tef2

Fba1

Mls1

Wt

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Effect of the FAR1 dosage on the proteome : growth in glucose-

supplemented media

exp

on

entia

l gro

wth

in S

CD

100

10

3 10

MW(Kda)

pI

Npl3

Pdc1

Krs1

Met6

Eft1Cdc19

Pgi1 Hom2Hom6

Eno1 Wrs1Gua1

Dld3

Tdh3

Stm1 Ydl124w

Rps2/Rps1ARpl8/Rps4/Rpl2

Fur1

Rib4

Egd2

Rps12 Rpl26

Rps18/Rps24/Rps17A

Adh1

Rps7A

Wt

Hxk1

29

FAR1 gene dosage: transcriptome vs proteome

Glucose Ethanol

mRNAsincreased 164 (3.0%) 87 (1.6%)decreased 130 (2.4%) 46 (0.8%)

Proteinsincreased 2 (0.5%) 7 (1.8%)decreased 12 (3.0%) 6 (1.5%)





far1 vs wt

FAR1 vs wt

FAR1 vs far1

Total genes called present in GeneChip® ~5500

Total proteins in 2D-page ~400

Glucose Ethanol

mRNAsincreaseddecreased

Proteinsincreaseddecreased





far1 vs wt

FAR1tet vs wt

FAR1tet vs far1

Total genes called present in GeneChip

Total proteins in 2D-page 400

Glucose Ethanol







far1 vs wt

FAR1 vs wt

FAR1 vs far1

Total genes called present in GeneChip® ~5500

Total proteins in 2D-page ~400

Glucose Ethanol







far1 vs wt

FAR1tet vs wt

FAR1tet vs far1

Total genes called present in GeneChip

Total proteins in 2D-page 400

+10

-10

-10

+10

Pro

tein

fold

change

glucose+10

-10

-10

+10

ethanol

mRNA fold change30

Ribosomal proteins are post-transcriptionally regulated in FAR1OE

strains

FAR1 overexpression stimulates rProt translation

= increase

= no change

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3232

FAR1tet cells Exponentially growing in glucose-supplemented media show a

coordinate induction of some ribosomal proteins

A loss of balance in ribosomal protein biogenesis could take place

An imbalance in the synthesis of the two ribosome subunits 40S and 60S can

induce ribosomal protein and rRNA synthesis by an autoregulation process

(Zhao et al., 2003)

FAR1tet mutant cells have more

rRNA too?

0,0

0,2

0,4

0,6

0,8

1,0

1,2

1,4

1,6

1,8

Wt Glucosio Delta far1

Glucosio

FAR1tet

Glucosio

Wt Etanolo Delta far1

Etanolo

FAR1tet

etanoloR

elat

ive

RN

A c

onte

ntfar1 FAR1tetWtfar1 FAR1tetWt

glucose ethanol

**

**

Rel

ativ

e R

NA

con

ten

t

Strain Mean

Wt 1far1 0,98 + 0,0255FAR1 tet 1,45 + 0,1332Wt 0,40 + 0,0631far1 0,45 + 0,0785FAR1 tet 0,52 + 0,0514

Glucose

Ethanol

FAR1 OVEREXPRESSION INCREASES CELLULAR RNA CONTENT

FAR1 dosage modulates metabolism

Acetate

Acetaldehyde

Acetyl-CoA

PyruvatePEP

GLU-6-PGA-3-P FRU-1,6-P

FBP1

PFK2

PFK1

TPI1

ENO1

ENO2

TDH3

PGK1

GPM1

TDH2

TDH1

FBA1

CDC19

PDC5

ADH1

ADH2

PDB1

PDX1

PDA1 PDA2

LPD1

PYK2

PDC1

PDC6

FRU-6-P PGI1

ALD2

ALD3

ALD5

ACS1

ACS2

HXK1

GLK1

HXK2

Ethanol

Glucose

mRNA+No change-

protein

+No change-

Exponential growth in ethanol

Exponential growth in glucose

Acetate

Acetaldehyde

Acetyl-CoA

Pyruvate

PEP

GLU-6-P

GA-3-P FRU-1,6-P

FBP1

PFK2

PFK1

TPI1

ENO1

ENO2

TDH3

PGK1

GPM1

TDH2

TDH1

FBA1

CDC19PDC5

ADH1

ADH2

PDB1

PDX1

PDA1 PDA2

LPD1

PYK2PDC1

PDC6

FRU-6-P

PGI1

ALD2

ALD3

ALD5

ACS1

ACS2

HXK1

GLK1

HXK2

Ethanol

Glucose

CIT2ACO1

IDH1,2

SDH3

FUM1

a-ketoglutarate

Succinate

KGD1,2

Oxaloacetate

SDH1,2,4

CIT3

CIT1

MDH1

Succinyl- CoA

LSC2

PYC1,2

PCK1

protein+No

change-

mRNA+No change-

33

FAR1 dosage affects expression of TOR-dependent Nitrogen Discrimination

Pathway

34

FAR1 dosage modulates PKA and TOR pathways

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FAR1 affects both growth and cell cycle

SIZER TIMER

Far1/Cln3 Whi5/SBF-MBF Sic1/Clb5

Critical Cell SizeCritical Cell SizeMetabolism building blocks

energy

Signaling PKA

Tor protein synthesis

Sfp1 ribosome biosynthesis

Onset S phase

Sic1 degradationSic1 degradation

(?)

Growth rateGrowth rate

DO

SA

GE

●

36

to be completed

NETWORKS AND CIRCUITS

General properties of organization:positive and negative feedbacks, threshold, switch, error connection, cell sizer etc.Hartwell et al, Nature, 1999

in collaboration with L. Farina, P. Palumbo, G. Mavelli

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Project ongoing

FROM THE CONCEPT MODEL TO THE REAL THING

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MODELS IN SYSTEMS BIOLOGY

• A model is a symbolic representation of reality which is able to foster understanding and to support decision-making

• A mathematical model is able to give a quantitative representation of a process and to make predictions

• Models in systems biology• structural models

• regulatory models

• dynamic models

39

Courtesy of A. Henney 40

A TOP-DOWN MODEL OF CELL CYCLE (2001)

Alberghina et al – Oncogene 20, 1128-1134, 2001

Alberghina et al – Current Genomics 5, 615-627, 2004

Two major areas of control

a cell sizer control (involving Cki and modulated by growth conditions) at the G1 to S transition

delays of mitosis execution, at metaphase/anaphase (End2) and at anaphase/telophase (End3), modulated by stress (DNA and spindle damages, conflicting metabolic signals, etc.)

M G1 S G2 M

cell sizer (Ps)

Master Control

START

C1

fast growthfast growth

cAMP cAMP hyperactivation hyperactivation

Resetting Subsystem

C2

END

MITOSIS

Cki

Pro Meta Ana Telo Kinesis

Cki

GROWTH STRESS

C3

fastfastgrowthgrowth

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ANALYSIS OF A SHIFT UP BY SIMULATION

The model correctly predicts, for cell population, during transitory state, a continuous increase of Ps and an increase in duration of budded phase, followed by its decrease to the new steady state

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Alberghina et al., Oncogene 20, 1128-1134, 2001Alberghina et al., J. Bacter. 180, 3864-3872

A proteomic analysis indicates that shift up cells undergo a stress response, conferming a connection between stress and delay of mitotic exit

Querin et al., J. Biol Chem, 2008

Courtesy of A. Henney43

THE SYSTEMS BIOLOGY APPROACH

STARTING NETWORK IDENTIFICATION WITH A MODULAR SYSTEMS BIOLOGY APPROACH

Alberghina L. et al, Curr. Genomics, 2004

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MATHEMATICAL MODEL OF THE G1 TO S TRANSITION

STARTING FROM SMALL DAUGHTER CELLS

45Barberis M, Klipp E. Vanoni M. and Alberghina L., PLoS Comput. Biol., 3, e64, 2007

Cln3 made in G1 proportional to cell mass

Far1

Cln3.CdK1

Cell sizer

Whi5SBF/MBF

Cln1.2. CdK1

Clb5.6. CdK1/Sic1

Sic1 degradation

G1 to S transition

Budding

THRESHOLD

DNA replication

timer

PsA Far1 amount endowed at the previous mitotic exit

(S-Cdk)

46A. Brummer, V. Zinzalla, C. Salazar, L. Alberghina and T. Hoefer, 2008, submitted

MODELING THE NETWORK CONTROLLING THE ONSET OF DNA REPLICATION

R. HEINRICH

Far1

Cln3 Whi5

Cdc28

Swi4

Swi6

Mbp1

Cln1

Cln2

Clb 5,6

Cdc34

Cdc53

Cdc4

Skp1

Sic1

Ck2

Sld3

Sld2

Dpb11

Cdc6

Cdt1

Mcm2-7

Cdc45

Psf3

Psf2Sld5

Psf1 GINS

11-3-2

SBF

MBF

DNA polym

●

●

●

●

Reconstruction of Protein Interactome of G1 to S transition

Complex

●

● Enzymatic Complex

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Westerhoff Westerhoff and friends: and friends: Amsterdam: 20081121 Amsterdam: 20081121 Trip to the virtual humanTrip to the virtual human

Blue(print) cellBlue(print) cell

TranslationTranslationMembrane trafficMembrane traffic

TranscriptionTranscription

Nuclear transportNuclear transport

transporttransport

Carbon metabolismCarbon metabolism

MitochondriaTCA cycleTCA cycle

cell

Energy metabolismEnergy metabolism

Westerhoff Westerhoff and friends: and friends: Amsterdam: 20081121 Amsterdam: 20081121 Trip to the virtual humanTrip to the virtual human

Blue(print) organismBlue(print) organism

HeartLungs

Brain

Eyes

Intestine

Model calibration, validation, comparison

KidneyCartilage

human

Figure 1. Service Oriented computing Architecture integrating the Web Services (indicated by the monitors) representing the human organs, through its Service Broker (SB).

Liver

University of Milano-Bicocca

L. AlberghinaM. Vanoni

R. RossiV. ZinzallaL. QuerinP. CoccettiA. MastrianiM. GraziolaF. TripodiF. SternieriD. PorroA. Di FonzoF. MagniS. FantinatoL. De GioiaP. FantucciR. SanvitoV. TsiarentsyevaC. CirulliN. CampbellM. Marchegiano

Max Planck Institute for Molecular Genetics,

BerlinE. Klipp

M. Barberis

THE MILANO-BICOCCA TEAM AND COLLABORATIONS

V. Zinzalla, University of Basel

T. Höfer, A. Brummer, C. Salazar Dfkz-Heidelberg

SETTING THE DURATION OF S PHASE

Experimental data• the length of S phase in fast growing cells is shorter than in slow

growing ones (in glucose TD = 104 min, S phase = 15 min; in ethanol TD = 314 min, S phase = 50 min)

• during the G1 to S transition fast growing cells have a much higher S-Cdk activity than slow growing ones

• the average DNA fragment length is about 46 Kb• the rate of DNA polymerization is about 2.9 Kb/min• for cells growing in nitrogen limitation (purine degrading enzymes?)

the rate of DNA polymerization decreases

From the model

duration of S phase =

= duration of firing (modulated by S-Cdk in the nucleus)

+ duration of DNA polymerization of average DNA fragment (modulated by growth conditions?)

KINETIC AND STRUCTURAL DETERMINANTS OF NETWORK ROBUSTNESS - 2

Catalytic use of the 11-3-2 activator ensures robust firing of replication

SYSTEMS BIOLOGY AND BIOTECHNOLOGY

• Drug discovery• Alterations of networks/circuits structure and/or

dynamics are taken to determine the diseased state• (Multi-lit) intervention to normalize diseased

networks for instance: neurodegenerative diseases

• Exploit specific differences between diseased and normal networks to selectively kill diseased cellsfor instance: cancer

• Bioprocesses/fermentations• Understanding of system-level properties of

metabolism will optimize substrate utilization and products formation

Lilia Alberghina Dept. of Biotechnology and Biosciences University of Milano-Bicocca Milan, Italy

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Transcript of Lilia Alberghina Dept. of Biotechnology and Biosciences University of Milano-Bicocca Milan, Italy